Modeling centrality measures in social network analysis using bi-criteria network flow optimization problems

Gómez, Daniel; Figueira, José Rui; Eusébio, Augusto

doi:10.1016/j.ejor.2012.11.027

Cited by 71 publications

(34 citation statements)

References 24 publications

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“…Unfortunately, data of a user's friends are very hard to obtain and we currently do not have these data. However, as Benoit and Van den Poel (2012) note, networkbased predictors (e.g., Gómez et al, 2013) may only improve predictions and hence our conclusions about the feasibility of usage increase prediction in social media are substantiated. Future research could try to obtain such data and improve on this study.…”

Section: Limitations and Directions For Future Researchmentioning

confidence: 90%

CRM in social media: Predicting increases in Facebook usage frequency

Ballings

Poel

2015

European Journal of Operational Research

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Section: Limitations and Directions For Future Researchmentioning

confidence: 90%

CRM in social media: Predicting increases in Facebook usage frequency

Ballings

Poel

2015

European Journal of Operational Research

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“…A novel approach of centrality measuring based on game theoretical concepts could be found in Gómez et al [20]. Gómez et al [21] illustrated how to extend the classical betweenness centrality measure at the point when the issue is demonstrated as a bi-criteria network flow optimization issue. Newman [22] extended the conventional conception of betweenness which implicitly assumed that information spread only along those shortest paths and proposed a betweenness measure that relaxed this assumption, including contributions from essentially all paths between nodes, not just the shortest.…”

Section: Literature Reviewmentioning

confidence: 99%

How to Identify the Most Powerful Node in Complex Networks? A Novel Entropy Centrality Approach

Qiao

Shan

Zhou

2017

Entropy

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Centrality is one of the most studied concepts in network analysis. Despite an abundance of methods for measuring centrality in social networks has been proposed, each approach exclusively characterizes limited parts of what it implies for an actor to be "vital" to the network. In this paper, a novel mechanism is proposed to quantitatively measure centrality using the re-defined entropy centrality model, which is based on decompositions of a graph into subgraphs and analysis on the entropy of neighbor nodes. By design, the re-defined entropy centrality which describes associations among node pairs and captures the process of influence propagation can be interpreted explained as a measure of actor potential for communication activity. We evaluate the efficiency of the proposed model by using four real-world datasets with varied sizes and densities and three artificial networks constructed by models including Barabasi-Albert, Erdos-Renyi and Watts-Stroggatz. The four datasets are Zachary's karate club, USAir97, Collaboration network and Email network URV respectively. Extensive experimental results prove the effectiveness of the proposed method.

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“…For instance, if we consider the correlation matrix of all commodity futures examined in this study and filter out the correlations above a certain value T, which are represented as edges in the network, the rest of the relationships based on correlations below this value are ignored. Both representations incorporate different ways to measure the centrality of a node (Newman, 2010;Gómez et al, 2013). Centrality is an important concept in network theory, yet there is no unique definition.…”

Section: Centrality Measurementmentioning

confidence: 99%

Information diffusion, cluster formation and entropy-based network dynamics in equity and commodity markets

Bekiros

Nguyen

Sandoval

et al. 2017

European Journal of Operational Research

126

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This paper investigates the dynamic causal linkages among U.S. equity and commodity futures markets via the utilization of complex network theory. We make use of rolling estimations of extended matrices and time-varying network topologies to reveal the temporal dimension of correlation and entropy relationships. A simulation analysis using randomized time series is also implemented to assess the impact of de-noising on the data dependence structure. We mainly show evidence of emphasized disparity of correlation and entropy-based centrality measurements for all markets between pre-and post-crisis periods. Our results enable the robust mapping of network influences and contagion effects whilst incorporating agent expectations.

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Modeling centrality measures in social network analysis using bi-criteria network flow optimization problems

Cited by 71 publications

References 24 publications

CRM in social media: Predicting increases in Facebook usage frequency

CRM in social media: Predicting increases in Facebook usage frequency

How to Identify the Most Powerful Node in Complex Networks? A Novel Entropy Centrality Approach

Information diffusion, cluster formation and entropy-based network dynamics in equity and commodity markets

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